





"""simple docstring"""





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   =  logging.get_logger()

@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = field(default_factory=_UpperCAmelCase   )
      _SCREAMING_SNAKE_CASE							      = field(default_factory=_UpperCAmelCase   )





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	List[str]:
            lowerCAmelCase    		=			len(list(m.modules()					)					) == 1 or isinstance(lowercase ,		nn.Convad					) or isinstance(lowercase ,		nn.BatchNormad					)
            if has_not_submodules:
                  self.traced.append(lowercase					)





      def __call__(			self ,		lowercase					)						->	Any:
            for m in self.module.modules():
                  self.handles.append(m.register_forward_hook(self._forward_hook					)					)
            self.module(lowercase					)
            [x.remove() for x in self.handles]
            return self





      @property
      def        _snake_case     (			self					)						->	List[Any]:
            # check the len of the state_dict keys to see if we have learnable params
            return list(filter(lambda lowercase					: len(list(x.state_dict().keys()					)					) > 0 ,		self.traced					)					)







@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 1
      _SCREAMING_SNAKE_CASE							      = field(default_factory=_UpperCAmelCase   )
      _SCREAMING_SNAKE_CASE							      = field(default_factory=_UpperCAmelCase   )
      _SCREAMING_SNAKE_CASE							      = True





      def __call__(			self ,		lowercase					)						->	Any:
            lowerCAmelCase    		=			Tracker(self.dest					)(lowercase					).parametrized
            lowerCAmelCase    		=			Tracker(self.src					)(lowercase					).parametrized

            lowerCAmelCase    		=			list(filter(lambda lowercase					: type(lowercase					) not in self.src_skip ,		lowercase					)					)
            lowerCAmelCase    		=			list(filter(lambda lowercase					: type(lowercase					) not in self.dest_skip ,		lowercase					)					)

            if len(lowercase					) != len(lowercase					) and self.raise_if_mismatch:
                  raise Exception(
                      f'Numbers of operations are different. Source module has {len(lowercase					)} operations while'
                      f' destination module has {len(lowercase					)}.'					)

            for dest_m, src_m in zip(lowercase ,		lowercase					):
                  dest_m.load_state_dict(src_m.state_dict()					)
                  if self.verbose == 1:
                        print(f'Transfered from={src_m} to={dest_m}'					)







class 						lowercase   (   nn.Module   ):





      def __init__(			self ,		lowercase					)						->	Union[str, Any]:
            super().__init__()

            lowerCAmelCase    		=			[]
            # - get the stem
            feature_blocks.append(("""conv1""", model.stem)					)
            # - get all the feature blocks
            for k, v in model.trunk_output.named_children():
                  assert k.startswith("""block"""					), f'Unexpected layer name {k}'
                  lowerCAmelCase    		=			len(lowercase					) + 1
                  feature_blocks.append((f'res{block_index}', v)					)

            lowerCAmelCase    		=			nn.ModuleDict(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Optional[Any]:
            return get_trunk_forward_outputs(
                lowercase ,		out_feat_keys=lowercase ,		feature_blocks=self._feature_blocks ,		)







class 						lowercase   (   _UpperCAmelCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	str:
            lowerCAmelCase    		=			x.split("""-"""					)
            return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)





      def __getitem__(			self ,		lowercase					)						->	Callable[[], Tuple[nn.Module, Dict]]:
            # default to timm!
            if x not in self:
                  lowerCAmelCase    		=			self.convert_name_to_timm(lowercase					)
                  lowerCAmelCase    		=			partial(lambda: (timm.create_model(lowercase ,		pretrained=lowercase					).eval(), None)					)

            else:
                  lowerCAmelCase    		=			super().__getitem__(lowercase					)

            return val







class 						lowercase   (   _UpperCAmelCase   ):





      def __getitem__(			self ,		lowercase					)						->	Callable[[], nn.Module]:
            if "seer" in x and "in1k" not in x:
                  lowerCAmelCase    		=			RegNetModel
            else:
                  lowerCAmelCase    		=			RegNetForImageClassification
            return val







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		List[Tuple[str, str]]     ):







      '''simple docstring'''




      for from_key, to_key in keys:
            lowerCAmelCase    		=			from_state_dict[from_key].clone()
            print(F'Copied key={from_key} to={to_key}'     )
      return to_state_dict


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Callable[[], nn.Module]				,		SCREAMING_SNAKE_CASE	:		Callable[[], nn.Module]				,		SCREAMING_SNAKE_CASE	:		RegNetConfig				,		SCREAMING_SNAKE_CASE	:		Path				,		SCREAMING_SNAKE_CASE	:		bool = True				,		):







      '''simple docstring'''




      print(F'Converting {name}...'     )
      with torch.no_grad():
            lowerCAmelCase		,						lowerCAmelCase    		=			from_model_func()
            lowerCAmelCase    		=			our_model_func(SCREAMING_SNAKE_CASE     ).eval()
            lowerCAmelCase    		=			ModuleTransfer(src=SCREAMING_SNAKE_CASE				,		dest=SCREAMING_SNAKE_CASE				,		raise_if_mismatch=SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			torch.randn((1, 3, 2_24, 2_24)     )
            module_transfer(SCREAMING_SNAKE_CASE     )

      if from_state_dict is not None:
            lowerCAmelCase    		=			[]
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  lowerCAmelCase    		=			[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
            lowerCAmelCase    		=			manually_copy_vissl_head(SCREAMING_SNAKE_CASE				,		our_model.state_dict()				,		SCREAMING_SNAKE_CASE     )
            our_model.load_state_dict(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			our_model(SCREAMING_SNAKE_CASE				,		output_hidden_states=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			(
          our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) else our_outputs.last_hidden_state
      )

      lowerCAmelCase    		=			from_model(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			from_output[-1] if type(SCREAMING_SNAKE_CASE     ) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            lowerCAmelCase    		=			our_outputs.hidden_states[-1]

      assert torch.allclose(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name				,		commit_message="""Add model"""				,		use_temp_dir=SCREAMING_SNAKE_CASE				,		)

            lowerCAmelCase    		=			2_24 if """seer""" not in name else 3_84
            # we can use the convnext one
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				,		size=SCREAMING_SNAKE_CASE     )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name				,		commit_message="""Add image processor"""				,		use_temp_dir=SCREAMING_SNAKE_CASE				,		)

            print(F'Pushed {name}'     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Path				,		SCREAMING_SNAKE_CASE	:		str = None				,		SCREAMING_SNAKE_CASE	:		bool = True     ):







      '''simple docstring'''




      lowerCAmelCase    		=			"""imagenet-1k-id2label.json"""
      lowerCAmelCase    		=			10_00
      lowerCAmelCase    		=			(1, num_labels)

      lowerCAmelCase    		=			"""huggingface/label-files"""
      lowerCAmelCase    		=			num_labels
      lowerCAmelCase    		=			json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""     )     )				,		"""r"""     )     )
      lowerCAmelCase    		=			{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}

      lowerCAmelCase    		=			idalabel
      lowerCAmelCase    		=			{v: k for k, v in idalabel.items()}

      lowerCAmelCase    		=			partial(SCREAMING_SNAKE_CASE				,		num_labels=SCREAMING_SNAKE_CASE				,		idalabel=SCREAMING_SNAKE_CASE				,		labelaid=SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			{
          """regnet-x-002""": ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 1_52, 3_68]				,		groups_width=8				,		layer_type="""x"""     ),
          """regnet-x-004""": ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]				,		hidden_sizes=[32, 64, 1_60, 3_84]				,		groups_width=16				,		layer_type="""x"""     ),
          """regnet-x-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]				,		hidden_sizes=[48, 96, 2_40, 5_28]				,		groups_width=24				,		layer_type="""x"""     ),
          """regnet-x-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]				,		hidden_sizes=[64, 1_28, 2_88, 6_72]				,		groups_width=16				,		layer_type="""x"""     ),
          """regnet-x-016""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]				,		hidden_sizes=[72, 1_68, 4_08, 9_12]				,		groups_width=24				,		layer_type="""x"""     ),
          """regnet-x-032""": ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]				,		hidden_sizes=[96, 1_92, 4_32, 10_08]				,		groups_width=48				,		layer_type="""x"""     ),
          """regnet-x-040""": ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]				,		hidden_sizes=[80, 2_40, 5_60, 13_60]				,		groups_width=40				,		layer_type="""x"""     ),
          """regnet-x-064""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]				,		hidden_sizes=[1_68, 3_92, 7_84, 16_24]				,		groups_width=56				,		layer_type="""x"""     ),
          """regnet-x-080""": ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]				,		hidden_sizes=[80, 2_40, 7_20, 19_20]				,		groups_width=1_20				,		layer_type="""x"""     ),
          """regnet-x-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]				,		hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,		groups_width=1_12				,		layer_type="""x"""     ),
          """regnet-x-160""": ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]				,		hidden_sizes=[2_56, 5_12, 8_96, 20_48]				,		groups_width=1_28				,		layer_type="""x"""     ),
          """regnet-x-320""": ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]				,		hidden_sizes=[3_36, 6_72, 13_44, 25_20]				,		groups_width=1_68				,		layer_type="""x"""     ),
          # y variant
          """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,		hidden_sizes=[24, 56, 1_52, 3_68]				,		groups_width=8     ),
          """regnet-y-004""": ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]				,		hidden_sizes=[48, 1_04, 2_08, 4_40]				,		groups_width=8     ),
          """regnet-y-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]				,		hidden_sizes=[48, 1_12, 2_56, 6_08]				,		groups_width=16     ),
          """regnet-y-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]				,		hidden_sizes=[64, 1_28, 3_20, 7_68]				,		groups_width=16     ),
          """regnet-y-016""": ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]				,		hidden_sizes=[48, 1_20, 3_36, 8_88]				,		groups_width=24     ),
          """regnet-y-032""": ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]				,		hidden_sizes=[72, 2_16, 5_76, 15_12]				,		groups_width=24     ),
          """regnet-y-040""": ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]				,		hidden_sizes=[1_28, 1_92, 5_12, 10_88]				,		groups_width=64     ),
          """regnet-y-064""": ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]				,		hidden_sizes=[1_44, 2_88, 5_76, 12_96]				,		groups_width=72     ),
          """regnet-y-080""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]				,		hidden_sizes=[1_68, 4_48, 8_96, 20_16]				,		groups_width=56     ),
          """regnet-y-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]				,		hidden_sizes=[2_24, 4_48, 8_96, 22_40]				,		groups_width=1_12     ),
          """regnet-y-160""": ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]				,		hidden_sizes=[2_24, 4_48, 12_32, 30_24]				,		groups_width=1_12     ),
          """regnet-y-320""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32     ),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32     ),
          """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]				,		hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,		groups_width=3_28     ),
          """regnet-y-1280-seer""": RegNetConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,		groups_width=2_64     ),
          """regnet-y-2560-seer""": RegNetConfig(
              depths=[3, 7, 16, 1]				,		hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,		groups_width=6_40     ),
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10     ),
          # finetuned on imagenet
          """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[2_32, 6_96, 13_92, 37_12]				,		groups_width=2_32     ),
          """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]				,		hidden_sizes=[3_28, 9_84, 19_68, 49_20]				,		groups_width=3_28     ),
          """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[5_28, 10_56, 29_04, 73_92]				,		groups_width=2_64     ),
          """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]				,		hidden_sizes=[6_40, 16_96, 25_44, 50_88]				,		groups_width=6_40     ),
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]				,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]				,		groups_width=10_10     ),
      }

      lowerCAmelCase    		=			NameToOurModelFuncMap()
      lowerCAmelCase    		=			NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Callable[[], nn.Module]     ) -> Tuple[nn.Module, Dict]:
            lowerCAmelCase    		=			torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE				,		model_dir=str(SCREAMING_SNAKE_CASE     )				,		map_location="""cpu"""     )
            lowerCAmelCase    		=			model_func()
            # check if we have a head, if yes add it
            lowerCAmelCase    		=			files["""classy_state_dict"""]["""base_model"""]["""model"""]
            lowerCAmelCase    		=			model_state_dict["""trunk"""]
            model.load_state_dict(SCREAMING_SNAKE_CASE     )
            return model.eval(), model_state_dict["heads"]

      # pretrained
      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )				,		)

      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )				,		)

      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )				,		)

      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""				,		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27				,		group_width=10_10				,		w_a=17_44				,		w_a=6_20.83				,		w_m=2.52     )     )     )				,		)

      # IN1K finetuned
      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )				,		)

      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )				,		)

      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )				,		)

      lowerCAmelCase    		=			partial(
          SCREAMING_SNAKE_CASE				,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""				,		lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27				,		group_width=10_10				,		w_a=17_44				,		w_a=6_20.83				,		w_m=2.52     )     )     )				,		)

      if model_name:
            convert_weight_and_push(
                SCREAMING_SNAKE_CASE				,		names_to_from_model_map[model_name]				,		names_to_ours_model_map[model_name]				,		names_to_config[model_name]				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      SCREAMING_SNAKE_CASE				,		names_to_from_model_map[model_name]				,		names_to_ours_model_map[model_name]				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		)
      return config, expected_shape


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default=None,
           type=str,
           help=(
               "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
               " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=Path,
           required=True,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument(
           "--push_to_hub",
           default=True,
           type=bool,
           required=False,
           help="If True, push model and image processor to the hub.",
       )

       SCREAMING_SNAKE_CASE__   =  parser.parse_args()

       SCREAMING_SNAKE_CASE__   =  args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)













"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






